{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## visualize words similarities via t-sne and sentence-transformer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I1229 16:35:13.771857 139849172682560 file_utils.py:40] PyTorch version 1.1.0 available.\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I1229 16:35:52.983521 139849172682560 SentenceTransformer.py:29] Load pretrained SentenceTransformer: bert-base-nli-mean-tokens\n",
      "I1229 16:35:52.985561 139849172682560 SentenceTransformer.py:32] Did not find a / or \\ in the name. Assume to download model from server\n",
      "I1229 16:35:53.160154 139849172682560 SentenceTransformer.py:68] Load SentenceTransformer from folder: /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip\n",
      "I1229 16:35:53.216943 139849172682560 configuration_utils.py:154] loading configuration file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/config.json\n",
      "I1229 16:35:53.218326 139849172682560 configuration_utils.py:174] Model config {\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"finetuning_task\": null,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"is_decoder\": false,\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"num_labels\": 2,\n",
      "  \"output_attentions\": false,\n",
      "  \"output_hidden_states\": false,\n",
      "  \"output_past\": true,\n",
      "  \"pruned_heads\": {},\n",
      "  \"torchscript\": false,\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_bfloat16\": false,\n",
      "  \"vocab_size\": 30522\n",
      "}\n",
      "\n",
      "I1229 16:35:53.219957 139849172682560 modeling_utils.py:390] loading weights file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/pytorch_model.bin\n",
      "I1229 16:37:43.619995 139849172682560 tokenization_utils.py:311] Model name '/home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT' not found in model shortcut name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese, bert-base-german-cased, bert-large-uncased-whole-word-masking, bert-large-cased-whole-word-masking, bert-large-uncased-whole-word-masking-finetuned-squad, bert-large-cased-whole-word-masking-finetuned-squad, bert-base-cased-finetuned-mrpc, bert-base-german-dbmdz-cased, bert-base-german-dbmdz-uncased). Assuming '/home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT' is a path or url to a directory containing tokenizer files.\n",
      "I1229 16:37:44.303762 139849172682560 tokenization_utils.py:340] Didn't find file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/tokenizer_config.json. We won't load it.\n",
      "I1229 16:37:44.306082 139849172682560 tokenization_utils.py:376] loading file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/vocab.txt\n",
      "I1229 16:37:44.307902 139849172682560 tokenization_utils.py:376] loading file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/added_tokens.json\n",
      "I1229 16:37:44.309458 139849172682560 tokenization_utils.py:376] loading file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/special_tokens_map.json\n",
      "I1229 16:37:44.310953 139849172682560 tokenization_utils.py:376] loading file None\n",
      "I1229 16:37:49.610430 139849172682560 SentenceTransformer.py:89] Use pytorch device: cpu\n"
     ]
    }
   ],
   "source": [
    "model = SentenceTransformer('bert-base-nli-mean-tokens')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.manifold import TSNE\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#parameters\n",
    "MIN = 5\n",
    "STOPWORDS = True\n",
    "SIZE = 10000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/reddit_batch1/batch.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.iloc[0:SIZE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn = df['topic'].unique().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words('english'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for transformer\n",
    "def clean_sen_trans(sent):\n",
    "    clean_list = ''\n",
    "    if STOPWORDS == True:\n",
    "        for word in sent.split():\n",
    "           # print(word)\n",
    "            if word not in stop_words:\n",
    "                word = re.sub('[\\W_]+','',word)\n",
    "                word = re.sub(' +', ' ', word)\n",
    "                clean_list +=word.lower()+str(' ')\n",
    "    else:\n",
    "        for word in sent.split():\n",
    "            word = re.sub('[\\W_]+','',word)\n",
    "            word = re.sub(' +', ' ', word)\n",
    "            clean_list +=word.lower()+str(' ')\n",
    "\n",
    "    return clean_list\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre1= []\n",
    "for i in df.sentence:\n",
    "    clean = clean_sen_trans(i)\n",
    "    if len(clean) > MIN:\n",
    "        pre1.append(clean)\n",
    "       \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = model.encode(pre1,show_progress_bar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_vocab(sens):\n",
    "    voc = []\n",
    "    for sen in sens:\n",
    "        for word in sen.split():\n",
    "            if not word in voc:\n",
    "                voc.append(word)\n",
    "    return voc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "voc = create_vocab(pre1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['walnut', 'select', 'australia', 'obsessions', 'lsd25', 'stop', 'pollack', 'friends']\n"
     ]
    }
   ],
   "source": [
    "queries=[]\n",
    "for i in range(nn):\n",
    "    queries.append(voc[np.random.randint(0,len(voc))])\n",
    "print(queries)\n",
    "query_embeddings = model.encode(queries,show_progress_bar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_embs = model.encode(voc,show_progress_bar=False)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import scipy.spatial\n",
    "# Find the closest nn words of the corpus for each query sentence based on cosine similarity\n",
    "closest_n = 15\n",
    "words_cl=[]\n",
    "embs_cl=[]\n",
    "for query, query_embedding in zip(queries, query_embeddings):\n",
    "  #  distances = scipy.spatial.distance.cdist([query_embedding], embeddings, \"cosine\")[0]\n",
    "    distances = scipy.spatial.distance.cdist([query_embedding], w_embs, \"cosine\")[0]\n",
    "    results = zip(range(len(distances)), distances)\n",
    "    results = sorted(results, key=lambda x: x[1])\n",
    "    pre_w_cl = []\n",
    "    pre_e_cl =[]\n",
    "    for idx, distance in results[0:closest_n]:\n",
    "        pre_w_cl.append(voc[idx])\n",
    "        pre_e_cl.append(w_embs[idx])\n",
    "       \n",
    "     #   print(pre1[idx].strip(), 1-distance)\n",
    "        #print(voc[idx].strip(), 1-distance)\n",
    "    words_cl.append(pre_w_cl)\n",
    "    embs_cl.append(pre_e_cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "top = closest_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "%matplotlib inline\n",
    "\n",
    "def plotsne(words,embs,perp):\n",
    "    \n",
    "    embs1 = np.array(embs).reshape(nn*top,768) ### reshape to nrandom*topn,DIM\n",
    "    tsne_2d = TSNE(perplexity=perp, n_components=2, init='pca', n_iter=3500, random_state=32)\n",
    "    emb_2d = np.array(tsne_2d.fit_transform(np.array(embs1)))    \n",
    "    \n",
    "    plt.figure(figsize=(16, 9))\n",
    "    space = np.expand_dims(np.linspace(0,1,nn),axis=1)\n",
    "    colors = cm.rainbow(space)\n",
    "    \n",
    "    \n",
    "    j = 0\n",
    "    jj = 0\n",
    "    for clust in words:\n",
    "        \n",
    "        for i in clust:\n",
    "\n",
    "            plt.annotate(i,xy=(emb_2d[j][0],emb_2d[j][1]),size=8,alpha=0.5)\n",
    "       \n",
    "            plt.scatter(emb_2d[j,0],emb_2d[j,1],alpha=0.8,c=colors[jj])\n",
    "            j+=1\n",
    "        jj += 1\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotsne(words_cl,embs_cl,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
